34,476 research outputs found
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
Fixed parameter tractable algorithms in combinatorial topology
To enumerate 3-manifold triangulations with a given property, one typically
begins with a set of potential face pairing graphs (also known as dual
1-skeletons), and then attempts to flesh each graph out into full
triangulations using an exponential-time enumeration. However, asymptotically
most graphs do not result in any 3-manifold triangulation, which leads to
significant "wasted time" in topological enumeration algorithms. Here we give a
new algorithm to determine whether a given face pairing graph supports any
3-manifold triangulation, and show this to be fixed parameter tractable in the
treewidth of the graph.
We extend this result to a "meta-theorem" by defining a broad class of
properties of triangulations, each with a corresponding fixed parameter
tractable existence algorithm. We explicitly implement this algorithm in the
most generic setting, and we identify heuristics that in practice are seen to
mitigate the large constants that so often occur in parameterised complexity,
highlighting the practicality of our techniques.Comment: 16 pages, 9 figure
Learning to Prove Safety over Parameterised Concurrent Systems (Full Version)
We revisit the classic problem of proving safety over parameterised
concurrent systems, i.e., an infinite family of finite-state concurrent systems
that are represented by some finite (symbolic) means. An example of such an
infinite family is a dining philosopher protocol with any number n of processes
(n being the parameter that defines the infinite family). Regular model
checking is a well-known generic framework for modelling parameterised
concurrent systems, where an infinite set of configurations (resp. transitions)
is represented by a regular set (resp. regular transducer). Although verifying
safety properties in the regular model checking framework is undecidable in
general, many sophisticated semi-algorithms have been developed in the past
fifteen years that can successfully prove safety in many practical instances.
In this paper, we propose a simple solution to synthesise regular inductive
invariants that makes use of Angluin's classic L* algorithm (and its variants).
We provide a termination guarantee when the set of configurations reachable
from a given set of initial configurations is regular. We have tested L*
algorithm on standard (as well as new) examples in regular model checking
including the dining philosopher protocol, the dining cryptographer protocol,
and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and
German). Our experiments show that, despite the simplicity of our solution, it
can perform at least as well as existing semi-algorithms.Comment: Full version of FMCAD'17 pape
Detecting Weakly Simple Polygons
A closed curve in the plane is weakly simple if it is the limit (in the
Fr\'echet metric) of a sequence of simple closed curves. We describe an
algorithm to determine whether a closed walk of length n in a simple plane
graph is weakly simple in O(n log n) time, improving an earlier O(n^3)-time
algorithm of Cortese et al. [Discrete Math. 2009]. As an immediate corollary,
we obtain the first efficient algorithm to determine whether an arbitrary
n-vertex polygon is weakly simple; our algorithm runs in O(n^2 log n) time. We
also describe algorithms that detect weak simplicity in O(n log n) time for two
interesting classes of polygons. Finally, we discuss subtle errors in several
previously published definitions of weak simplicity.Comment: 25 pages and 13 figures, submitted to SODA 201
Memory lower bounds for deterministic self-stabilization
In the context of self-stabilization, a \emph{silent} algorithm guarantees
that the register of every node does not change once the algorithm has
stabilized. At the end of the 90's, Dolev et al. [Acta Inf. '99] showed that,
for finding the centers of a graph, for electing a leader, or for constructing
a spanning tree, every silent algorithm must use a memory of
bits per register in -node networks. Similarly, Korman et al. [Dist. Comp.
'07] proved, using the notion of proof-labeling-scheme, that, for constructing
a minimum-weight spanning trees (MST), every silent algorithm must use a memory
of bits per register. It follows that requiring the algorithm
to be silent has a cost in terms of memory space, while, in the context of
self-stabilization, where every node constantly checks the states of its
neighbors, the silence property can be of limited practical interest. In fact,
it is known that relaxing this requirement results in algorithms with smaller
space-complexity.
In this paper, we are aiming at measuring how much gain in terms of memory
can be expected by using arbitrary self-stabilizing algorithms, not necessarily
silent. To our knowledge, the only known lower bound on the memory requirement
for general algorithms, also established at the end of the 90's, is due to
Beauquier et al.~[PODC '99] who proved that registers of constant size are not
sufficient for leader election algorithms. We improve this result by
establishing a tight lower bound of bits per
register for self-stabilizing algorithms solving -coloring or
constructing a spanning tree in networks of maximum degree~. The lower
bound bits per register also holds for leader election
Clustering Memes in Social Media
The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM'13), 201
- …